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Improving NDVI time series class separation using an extended Kalman filter

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dc.contributor.author Kleynhans, W
dc.contributor.author Olivier, JC
dc.contributor.author Salmon, BP
dc.contributor.author Wessels, Konrad J
dc.contributor.author Van den Bergh, F
dc.date.accessioned 2010-03-08T10:01:59Z
dc.date.available 2010-03-08T10:01:59Z
dc.date.issued 2009-07
dc.identifier.citation Kleynhans W, Olivier JC, Salmon, BP, Wessels, KJ and Van den Berg, F. 2009. Improving NDVI time series class separation using an extended Kalman filter. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009, pp 1-4 en
dc.identifier.isbn 9781424433940
dc.identifier.uri http://hdl.handle.net/10204/3980
dc.description IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009 en
dc.description.abstract It is proposed that the NDVI time series derived from MODIS multitemporal remote sensing data can be modelled as a triply (mean, phase and amplitude) modulated cosine function. A non-linear Extended Kalman Filter was developed to estimate the parameters of the modulated cosine function as a function of time. It was shown that the maximum separability of the parameters for different vegetation land cover was better than that of a spectral method based on the Fast Fourier Transform (FFT). Thus it is theorized that the cosine function parameters estimated using the EKF is superior for both classifying land cover and detecting change over time when compared to methods based on the FFT. Results from two study areas in Southern Africa are provided to show the improved separability using MODIS data. en
dc.language.iso en en
dc.publisher IEEE en
dc.subject NDVI en
dc.subject Kalman Filter en
dc.subject Fast fourier transform en
dc.subject MODIS data en
dc.subject Remote sensing en
dc.subject Geosciences en
dc.title Improving NDVI time series class separation using an extended Kalman filter en
dc.type Conference Presentation en
dc.identifier.apacitation Kleynhans, W., Olivier, J., Salmon, B., Wessels, K. J., & Van den Bergh, F. (2009). Improving NDVI time series class separation using an extended Kalman filter. IEEE. http://hdl.handle.net/10204/3980 en_ZA
dc.identifier.chicagocitation Kleynhans, W, JC Olivier, BP Salmon, Konrad J Wessels, and F Van den Bergh. "Improving NDVI time series class separation using an extended Kalman filter." (2009): http://hdl.handle.net/10204/3980 en_ZA
dc.identifier.vancouvercitation Kleynhans W, Olivier J, Salmon B, Wessels KJ, Van den Bergh F, Improving NDVI time series class separation using an extended Kalman filter; IEEE; 2009. http://hdl.handle.net/10204/3980 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Kleynhans, W AU - Olivier, JC AU - Salmon, BP AU - Wessels, Konrad J AU - Van den Bergh, F AB - It is proposed that the NDVI time series derived from MODIS multitemporal remote sensing data can be modelled as a triply (mean, phase and amplitude) modulated cosine function. A non-linear Extended Kalman Filter was developed to estimate the parameters of the modulated cosine function as a function of time. It was shown that the maximum separability of the parameters for different vegetation land cover was better than that of a spectral method based on the Fast Fourier Transform (FFT). Thus it is theorized that the cosine function parameters estimated using the EKF is superior for both classifying land cover and detecting change over time when compared to methods based on the FFT. Results from two study areas in Southern Africa are provided to show the improved separability using MODIS data. DA - 2009-07 DB - ResearchSpace DP - CSIR KW - NDVI KW - Kalman Filter KW - Fast fourier transform KW - MODIS data KW - Remote sensing KW - Geosciences LK - https://researchspace.csir.co.za PY - 2009 SM - 9781424433940 T1 - Improving NDVI time series class separation using an extended Kalman filter TI - Improving NDVI time series class separation using an extended Kalman filter UR - http://hdl.handle.net/10204/3980 ER - en_ZA


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